Operator Learning deals with mapping from functions to functions and commonly used for PDEs. Operator learning can be deployed in computer vision as images are RGB-valued functions on a 2D plane. This continuous generalization allows us to permeate benefits from operators. Recent advances in operator learning include DeepONet [1]} that learns the coefficients and basis of the operators, and neural operators [2]} that are parameterized by integral operators. In this work, we adopt Fourier neural operators [3]} that implement global convolution via FFT which has been very successful for solving nonlinear and chaotic PDEs.
